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http://dx.doi.org/10.13161/kibim.2019.9.1.042

Generative Model of Acceleration Data for Deep Learning-based Damage Detection for Bridges Using Generative Adversarial Network  

Lee, Kanghyeok (인하대학교 토목공학과)
Shin, Do Hyoung (인하대학교 사회인프라공학과)
Publication Information
Journal of KIBIM / v.9, no.1, 2019 , pp. 42-51 More about this Journal
Abstract
Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.
Keywords
Generative Adversarial Network; Generative Model; Damage Detection; Digital Twin; Maintenance;
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